Influence of Channel Selection and Subject’s Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 2; p. 899
• Main Authors: Nazih, Waleed, Shahin, Mostafa, Eldesouki, Mohamed I., Ahmed, Beena
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.01.2023; MDPI
• Subjects: Accuracy; Adult; Age; Algorithms; Child; Classification; Datasets; Deep learning; EEG; electroencephalogram; Electroencephalography; Electroencephalography - methods; Eye movements; Humans; Infant, Newborn; Machine learning; Neural networks; pediatric; Pediatrics; Reproducibility of Results; Sleep; Sleep disorders; sleep stage scoring; Sleep Stages; Support vector machines; deep learning; sleep stage scoring; EEG; electroencephalogram; pediatric

The electroencephalogram (EEG) signal is a key parameter used to identify the different sleep stages present in an overnight sleep recording. Sleep staging is crucial in the diagnosis of several sleep disorders; however, the manual annotation of the EEG signal is a costly and time-consuming process....